In order to fully achieve energy saving goals, it is necessary to establish a comprehensive evaluation system for carbon reduction in transmission and transformation projects. Subsequently, weights were assigned to these indicators using a combination of the fuzzy analytical hierarchy process (FAHP) and the entropy weight method (EWM) through both subjective and objective methods. Finally, the ultimate weights were obtained by applying the principle of minimum information. During the construction of the evaluation model, the rank–sum ratio (RSR) method was introduced into the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for approximating ideal solution ranking. And the Euclidean distance in TOPSIS was replaced with standardized Euclidean distance, effectively avoiding evaluation discrepancies caused by different dimensions. The modified TOPSIS-RSR method was utilized to evaluate and rank power transmission and transformation projects in four regions. By comparing the test values of the two models, the superiority of the enhanced model was confirmed. Furthermore, the GM (1,1) model is used to predict the electricity sales volume of the optimal ranking area. This evaluation model can also be applied to the benefit evaluation of carbon reduction benefits in power transmission and transformation projects in other regions.

Transmission lines serve as pivotal equipment within the power system. Conductors, the primary medium for power transmission and distribution, directly influence the construction cost, operational performance, and long-term benefits of transmission line projects. This study first provides a detailed introduction to the life cycle cost of transmission lines. It utilizes linear regression analysis, the grey model, and the autoregressive integrated moving average model to forecast the electricity sales benefit and quantify the carbon reduction benefits of energy-saving conductors through a methodology. Through the life cycle cost model, we found that operating costs, particularly energy loss costs, dominate the total expenses, accounting for 65% to 66.2%. The JLHA3-425 scheme offers the lowest life cycle cost of 22,891.66 k$. Comparing economic indicators like ENPV, EIRR, and DPP confirmed that the JLHA3-425 medium-strength aluminum alloy stranded wire emerged as the most economically viable option among the evaluated schemes, holding substantial promise for fostering economic and environmental sustainability in electrical power transmission.

To investigate the economic impact of carbon reduction technology on power transmission and transformation projects, carbon reduction technologies adapted to power transmission and transformation projects were investigated, and the evaluation indicator system for cost-benefit in power transmission and transformation projects was established based on AHP (Analytic Hierarchy Process) and EWM (Entropy Weight Method). Taking 110 kV and 220 kV transmission and transformation projects in a province located in northwest China as an example, a sensitivity analysis was carried out for the weight of each carbon reduction technology. Additionally, based on the evaluation indicator system, eight alternative carbon reduction schemes were proposed, and the net present value and scheme scores were analyzed with TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution). The results showed that in the criterion layer of the 110 kV case, the highest proportion of weight is “high-efficiency transformer”, accounting for 34.12%, and in the indicator layer, the highest proportion of weight is “ parasitic losses”, accounting for 12.35%. After conducting error analysis on the 110 kV and 220 kV cases, it was found that the errors were within an acceptable range both in the criterion layer and index layer. Moreover, it is expected to achieve greater economic benefits through lower costs according to Scheme VI, and due to the limitations of carbon reduction technologies and the lack of a relevant policy system, it is difficult to achieve the goal of covering costs with benefits for the eight schemes studied.

In response to China’s dual-carbon strategy, this study proposes a comprehensive analytical framework to identify the evolutionary pathways of key policy tasks in developing a new-type power system. A dual-channel data acquisition process was designed to extract, standardize, and segment policy documents and online texts into a unified corpus. A multi-label BERT classification model was then developed, incorporating domain-specific terminology injection, label-wise attention, dynamic threshold scanning, and imbalance-aware weighting. The model was trained and validated on 200 energy news articles, 100 official policy releases, and 10 strategic planning documents. By the 10th epoch, it achieved convergence with a Macro-F1 of 0.831, Micro-F1 of 0.849, and Samples-F1 of 0.855. Ablation studies confirmed the significant performance gain over simplified configurations. Structural label analysis showed “Build system-friendly new energy power stations” was the most frequent label (107 in plans, 80 in news, 24 in policies) and had the highest co-occurrence (81 times) with “Optimize and strengthen the main grid framework.” The label co-occurrence network revealed multi-layered couplings across generation, transmission, and storage. The Priority Evaluation Index (PEI) further identified “Build shared energy storage power stations” as a structurally central task (centrality = 0.71) despite its lower frequency, highlighting its latent strategic importance. Within the domain of national-level public policy and planning documents, the proposed framework shows reliable and reusable performance. Generalization to sub-national and project-level corpora is left for future work, where we will extend the corpus and reassess robustness without altering the core methodology.

Under the twin imperatives of climate change mitigation and sustainable development, achieving a low-carbon transformation of power systems has become a national priority. To clarify this objective, China issued the Blue Book on the Development of New Power System, which comprehensively defines the guiding concepts and characteristic features of a new power system. In this study, natural language processing-based keyword extraction techniques were applied to the document, employing both the TF-IDF and TextRank algorithms to identify its high-frequency terms as characteristic keywords. These keywords were then used as topic queries in the Web of Science Core Collection, yielding 1568 relevant publications. CiteSpace was employed to perform a bibliometric analysis of these records, extracting research hotspots in the new power system domain and tracing their evolutionary trajectories. The analysis revealed that “renewable energy” appeared 247 times as the core high-frequency term, while “energy storage” exhibited both high frequency and high centrality, acting as a bridge across multiple subfields. This pattern suggests that research in the new power system field has evolved from a foundation in renewable energy and storage toward smart grids, market mechanisms, carbon capture, and artificial intelligence applications. Taken together, these results indicate that early research was primarily grounded in renewable energy and storage technologies, which provided the technical basis for subsequent exploration of smart grids and market mechanisms. In the more recent stage, under the dual-carbon policy and digital intelligence imperatives, research hotspots have further expanded toward carbon capture, utilization, and storage (CCUS) and artificial intelligence applications. Looking ahead, interdisciplinary studies focusing on intelligent dispatch and low-carbon transition are poised to emerge as the next major research frontier.

To alleviate renewable energy curtailment and the high operating costs arising from the temporal and spatial mismatch of distributed generation, this paper develops a cross-park dispatch optimization approach for power systems under the joint participation of carbon trading and green certificate trading (GCT). The proposed approach aims to improve system flexibility and economic performance in coordinated multi-park operation. Specifically, adjustable resources in different parks are dispatched in a coordinated manner, and the total comprehensive operating cost is taken as the optimization objective. In addition, the Alternating Direction Method of Multipliers (ADMM) is adopted to determine inter-park electricity trading prices and exchanged power in a distributed framework. Furthermore, an asymmetric bargaining model is introduced to distribute the cooperative benefits, ensuring a balance between fairness and incentive compatibility. Simulation results demonstrate that inter-park electricity interaction reduces generation costs by 5.29%. The integration of carbon and green certificate trading further reduces costs by 7.4%. After asymmetric bargaining-based benefit allocation, the operating costs of parks with higher contributions decrease by up to 10.34%. The results conclude that the proposed strategy effectively leverages the complementary advantages of multi-park resources and optimizes the synergy between carbon markets, green certificate markets, and physical dispatch.